Prognostic robustness of serum creatinine based AKI definitions in patients with sepsis: a prospective cohort study

Background: It is unclear how modifications in the way to calculate serum creatinine (sCr) increase and in the cut-off value applied, influences the prognostic value of Acute Kidney Injury (AKI). We wanted to evaluate whether these modifications alter the prognostic value of AKI for prediction of mortality at 3 months, 1 and 2 years. Methods: We prospectively included 195 septic patients and evaluated the prognostic value of AKI by using three different algorithms to calculate sCr increase: either as the difference between the highest value in the first 24 h after ICU admission and a pre-admission historical (Delta HIS) or an estimated (Delta EST) baseline value, or by subtracting the ICU admission value from the sCr value 24 h after ICU admission (Delta ADM). Different cut-off levels of sCr increase (0.1, 0.2, 0.3, 0.4 and 0.5 mg/dl) were evaluated. Results: Mortality at 3 months, 1 and 2 years in AKI defined as Delta ADM > 0.3 mg/dl was 48.1 %, 63.0 % and 63.0 % vs 27.7 %, 39.8 % and 47.6 % in no AKI respectively (OR(95%CI): 2.42(1.06-5.54), 2.58(1.11-5.97) and 1.87(0.81-4.33); 0.3 mg/dl was the lowest cut-off value that was discriminatory. When AKI was defined as Delta HIS > 0.3 mg/dl or Delta EST > 0.3 mg/dl, there was no significant difference in mortality between AKI and no AKI. Conclusions: The prognostic value of a 0.3 mg/dl increase in sCr, on mortality in sepsis, depends on how this sCr increase is calculated. Only if the evolution of serum creatinine over the first 24 h after ICU admission is taken into account, an association with mortality is found

Homogeneous datasets of triple negative breast cancers enable the identification of novel prognostic and predictive signatures

Background: Current prognostic gene signatures for breast cancer mainly reflect proliferation status and have limited value in triple-negative (TNBC) cancers. The identification of prognostic signatures from TNBC cohorts was limited in the past due to small sample sizes. Methodology/Principal Findings: We assembled all currently publically available TNBC gene expression datasets generated on Affymetrix gene chips. Inter-laboratory variation was minimized by filtering methods for both samples and genes. Supervised analysis was performed to identify prognostic signatures from 394 cases which were subsequently tested on an independent validation cohort (n = 261 cases). Conclusions/Significance: Using two distinct false discovery rate thresholds, 25% and <3.5%, a larger (n = 264 probesets) and a smaller (n = 26 probesets) prognostic gene sets were identified and used as prognostic predictors. Most of these genes were positively associated with poor prognosis and correlated to metagenes for inflammation and angiogenesis. No correlation to other previously published prognostic signatures (recurrence score, genomic grade index, 70-gene signature, wound response signature, 7-gene immune response module, stroma derived prognostic predictor, and a medullary like signature) was observed. In multivariate analyses in the validation cohort the two signatures showed hazard ratios of 4.03 (95% confidence interval [CI] 1.71–9.48; P = 0.001) and 4.08 (95% CI 1.79–9.28; P = 0.001), respectively. The 10-year event-free survival was 70% for the good risk and 20% for the high risk group. The 26-gene signatures had modest predictive value (AUC = 0.588) to predict response to neoadjuvant chemotherapy, however, the combination of a B-cell metagene with the prognostic signatures increased its response predictive value. We identified a 264-gene prognostic signature for TNBC which is unrelated to previously known prognostic signatures

Survival prediction in mesothelioma using a scalable lasso regression model: instructions for use and initial performance using clinical predictors

Introduction: Accurate prognostication is difficult in malignant pleural mesothelioma (MPM). We developed a set of robust computational models to quantify the prognostic value of routinely available clinical data, which form the basis of published MPM prognostic models. Methods: Data regarding 269 patients with MPM were allocated to balanced training (n=169) and validation sets (n=100). Prognostic signatures (minimal length best performing multivariate trained models) were generated by least absolute shrinkage and selection operator regression for overall survival (OS), OS &lt;6 months and OS &lt;12 months. OS prediction was quantified using Somers DXY statistic, which varies from 0 to 1, with increasing concordance between observed and predicted outcomes. 6-month survival and 12-month survival were described by area under the curve (AUC) scores. Results: Median OS was 270 (IQR 140–450) days. The primary OS model assigned high weights to four predictors: age, performance status, white cell count and serum albumin, and after cross-validation performed significantly better than would be expected by chance (mean DXY0.332 (±0.019)). However, validation set DXY was only 0.221 (0.0935–0.346), equating to a 22% improvement in survival prediction than would be expected by chance. The 6-month and 12-month OS signatures included the same four predictors, in addition to epithelioid histology plus platelets and epithelioid histology plus C-reactive protein (mean AUC 0.758 (±0.022) and 0.737 (±0.012), respectively). The &lt;6-month OS model demonstrated 74% sensitivity and 68% specificity. The &lt;12-month OS model demonstrated 63% sensitivity and 79% specificity. Model content and performance were generally comparable with previous studies. Conclusions: The prognostic value of the basic clinical information contained in these, and previously published models, is fundamentally of limited value in accurately predicting MPM prognosis. The methods described are suitable for expansion using emerging predictors, including tumour genomics and volumetric staging

Impact of depth of response on survival in patients treated with cobimetinib ± vemurafenib: pooled analysis of BRIM-2, BRIM-3, BRIM-7 and coBRIM.

BackgroundThis pooled analysis investigated the prognostic value of depth of response in two cohorts of patients with BRAFV600-mutated metastatic melanoma treated with vemurafenib or cobimetinib plus vemurafenib.MethodsThe data were pooled from BRIM-2, BRIM-3, BRIM-7 and coBRIM. Association of depth of response with survival was estimated by Cox proportional hazards regression, adjusted for clinically relevant covariates. Depth of response was analysed in previously identified prognostic subgroups based on disease characteristics and gene signatures.ResultsGreater tumour reduction and longer time to maximal response were significantly associated with longer progression-free survival (PFS) and overall survival (OS) when evaluated as continuous variables. Patients with the deepest responses had long-lasting survival outcomes (median PFS: 14 months; OS: 32 months with vemurafenib; not estimable with cobimetinib plus vemurafenib). Cobimetinib plus vemurafenib improved depth of response versus vemurafenib monotherapy regardless of other prognostic factors, including gene signatures.ConclusionsGreater depth of response was associated with improved survival, supporting its utility as a measure of treatment efficacy in melanoma and further evaluation of its incorporation into existing prognostic models. Cobimetinib plus vemurafenib improved outcomes across quartiles of response regardless of prognostic factors or gene signatures and provided durable survival benefits in patients with deep responses

Prognostic value of thyroid hormone levels in patients evaluated for liver transplantation

The thyroid hormones T4, T3, rT3 and TSH were assayed in 134 adult patients evaluated and accepted as potential liver transplant candidates at the Universty of Pittsburgh from March, 1981 to December, 1983. The subsequent course of these patients was evaluated with respect to the levels of these hormones obtained at the time of acceptance for transplantation. T4 levels were increased significantly while their T3 levels were reduced (both p < 0.01) in those who survived and were discharged home as compared to either those who died waiting to be transplanted or died following the procedure. As a result, the ratio of T3/T4 was reduced markedly (p < 0.01) in those who were transplanted and survived as compared to those not transplanted or dying following transplantation. Importantly, the rT3 levels clearly separated (p < 0.01) those who would die prior to transplantation from those who would survive to be transplanted. Finally, the ratio rT3/T3 even more clearly separates those who will die prior to transplantation (p < 0.01) from the other two groups. These data suggest that thyroid hormone levels, particularly rT3 levels, might be useful in setting priorities for which patients referred for a transplantation evaluation should be accepted into the program and in determining who among accepted patients should be operated upon in preference to others also accepted and waiting to be transplanted. Copyright © 1985 American Association for the Study of Liver Disease

A Robust Gene Expression Prognostic Signature for Overall Survival in High-Grade Serous Ovarian Cancer.

The objective of this research was to develop a robust gene expression-based prognostic signature and scoring system for predicting overall survival (OS) of patients with high-grade serous ovarian cancer (HGSOC). Transcriptomic data of HGSOC patients were obtained from six independent studies in the NCBI GEO database. Genes significantly deregulated and associated with OS in HGSOCs were selected using GEO2R and Kaplan-Meier analysis with log-rank testing, respectively. Enrichment analysis for biological processes and pathways was performed using Gene Ontology analysis. A resampling/cross-validation method with Cox regression analysis was used to identify a novel gene expression-based signature associated with OS, and a prognostic scoring system was developed and further validated in nine independent HGSOC datasets. We first identified 488 significantly deregulated genes in HGSOC patients, of which 232 were found to be significantly associated with their OS. These genes were significantly enriched for cell cycle division, epithelial cell differentiation, p53 signaling pathway, vasculature development, and other processes. A novel 11-gene prognostic signature was identified and a prognostic scoring system was developed, which robustly predicted OS in HGSOC patients in 100 sampling test sets. The scoring system was further validated successfully in nine additional HGSOC public datasets. In conclusion, our integrative bioinformatics study combining transcriptomic and clinical data established an 11-gene prognostic signature for robust and reproducible prediction of OS in HGSOC patients. This signature could be of clinical value for guiding therapeutic selection and individualized treatment

The prognostic value of cortical magnetic stimulation in acute middle cerebral artery infarction compared to other parameters

The prognostic value of magnetic evoked potentials (MEP), somatosensory evoked potentials (SSEP), age and radiological parameters was determined in 50 patients with acute middle cerebral artery infarction. We performed MEP and SSEP within 4 days and after 6 weeks and 3 months of the infarction and assessed clinical improvement by using the Barthel index (BI) and the Rankin scale. The localization and extent of the infarction was investigated by CT scanning or NMR. All parameters were correlated to clinical outcome and the prognostic significance of each parameter in addition to BI was determined. MEP, SSEP, and age were valuable prognostic parameters in predicting stroke outcome when used together with the BI. However, in stepwise regression analysis using all parameters simultaneously, only MEP and age significantly contributed to clinical outcome in addition to BI. Patients showed a better outcome when their MEP was normal or delayed, measured within 4 days of the infarction, compared to patients with absent MEP. Clinical outcome was better at a younger age

Prognostic value of routine laboratory variables in prediction of breast cancer recurrence.

The prognostic value of routine laboratory variables in breast cancer has been largely overlooked. Based on laboratory tests commonly performed in clinical practice, we aimed to develop a new model to predict disease free survival (DFS) after surgical removal of primary breast cancer. In a cohort of 1,596 breast cancer patients, we analyzed the associations of 33 laboratory variables with patient DFS. Based on 3 significant laboratory variables (hemoglobin, alkaline phosphatase, and international normalized ratio), together with important demographic and clinical variables, we developed a prognostic model, achieving the area under the curve of 0.79. We categorized patients into 3 risk groups according to the prognostic index developed from the final model. Compared with the patients in the low-risk group, those in the medium- and high-risk group had a significantly increased risk of recurrence with a hazard ratio (HR) of 1.75 (95% confidence interval [CI] 1.30-2.38) and 4.66 (95% CI 3.54-6.14), respectively. The results from the training set were validated in the testing set. Overall, our prognostic model incorporating readily available routine laboratory tests is powerful in identifying breast cancer patients who are at high risk of recurrence. Further study is warranted to validate its clinical application

Prognostic value of troponins in acute coronary syndrome depends upon patient age

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